Computing tips for awesome research and an easy life

Robert Turner, University of Sheffield RSE Team September, 2021

Acknowledgements

Contains elements from Reproducible Research Data and Project Management in R, by Anna Krystalli and from Methods in Research Software Engineering by David Wilby.

About me

Mix of software engineering and research experience.

RSE Team

13 RSEs, 35 projects / year worth ~£11m total

This presentation is flawed

Focusses on what to do, not how to do it.

In this session…

Discussion

What are the characteristics of well engineered research software?

Link to interactive doc

“Good” research software

• Version control
• Automated tests
• Controlled execution environment
• Documentation
• Parameterised

https://joss.readthedocs.io/en/latest/review_checklist.html

Data Management

Data Management Plan

• Start early. Make an RDM plan before collecting data.
• Anticipate data products as part of your thesis outputs.
• Think about what technologies to use.

Own your data

Act as though every short term study will become a long term one @tomjwebb. Needs to be reproducible in 3, 20, 100 yrs

— Oceans Initiative (@oceansresearch) January 16, 2015

Take initiative & responsibility. Think long term.

Spreadsheets?

@tomjwebb stay away from excel at all costs?

— Timothée Poisot (@tpoi) January 16, 2015

Do you agree?

Excel

THRILLED by this announcement by the Human Gene Nomenclature Committee. pic.twitter.com/BqLIOMm69d

— Janna Hutz (@jannahutz) August 4, 2020

But good for data viewing / entry, sometimes, perhaps…

Databases

@tomjwebb Entering via a database management system (e.g., Access, Filemaker) can make entry easier & help prevent data entry errors @tpoi

— Ethan White (@ethanwhite) January 16, 2015

@ethanwhite +1 Enforcing data types, options from selection etc, just some useful things a DB gives you, if you turn them on @tomjwebb @tpoi

— Gavin Simpson (@ucfagls) January 16, 2015

@tomjwebb it also prevents a lot of different bad practices. It is possible to do some of this in Excel. @tpoi

— Ethan White (@ethanwhite) January 16, 2015

Have a look at the Data Carpentry SQL for Ecology lesson

Data formats

• .csv: comma separated values.
• .tsv: tab separated values.
• .txt: no formatting specified.

@tomjwebb It has to be interoperability/openness - can I read your data with whatever I use, without having to convert it?

— Paul Swaddle (@paul_swaddle) January 16, 2015

more unusual formats will need instructions on use.

Ensure data is machine readable

Andrea De Santis, unsplash.com

bad

bad

good

ok

• could help data entry
• .csv or .tsv copy would need to be saved.

Basic quality control

Use good null values, missing values are a fact of life:

• Usually, best solution is to leave blank
• NA or NULL are also good options
• NEVER use 0. Avoid numbers like -999
• Don’t make up your own code for missing values

@tomjwebb don't, not even with a barge pole, not for one second, touch or otherwise edit the raw data files. Do any manipulations in script

— Gavin Simpson (@ucfagls) January 16, 2015

@tomjwebb @srsupp Keep one or a few good master data files (per data collection of interest), and code your formatting with good annotation.

— Desiree Narango (@DLNarango) January 16, 2015

Data security

Raw data are sacrosanct

Give yourself less rope

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• It’s a good idea to revoke your own write permission to the raw data file.
• Then you can’t accidentally edit it.
• It also makes it harder to do manual edits in a moment of weakness, when you know you should just add a line to your data cleaning script.

Photo by Jon Moore, unsplash.com

Know your main copies

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• identify the main copy of files
• keep it safe and and accessible
• consider version control
• consider centralising

source: Pexels CC0

How to avoid catastrophes

@tomjwebb Back it up

— Ben Bond-Lamberty (@BenBondLamberty) January 16, 2015

Backup: on disk

• consider using backup software like Time Machine (mac) or File History (Windows 10)

Backup: in the cloud

• dropbox, googledrive etc.
• if installed on your system, can programmatically access them through R
• some version control

Backup: the Open Science Framework osf.io

• version controlled
• easily shareable
• works with other apps (eg googledrive, github)

Backup: Github

• most solid version control.
• keep everything in one project folder.
• Can be problematic with really large files.